Power-law tail in lag time distribution underlies bacterial persistence

Diversification of Pseudomonas aeruginosa After Inhaled Tobramycin Therapy of Cystic Fibrosis Patients: Genotypic and Phenotypic Characteristics of Paired Pre- and Post-Treatment Isolates

This study examines the impact of inhaled tobramycin therapy on the within-host changes in P. aeruginosa strains isolated from Bulgarian patients with CF prior to and post treatment. Genotypic comparison by RAPD-PCR indicated that most of the pre-treatment isolates had a high similarity and were genetically comparatively close to strains from other countries with known increased morbidity or treatment requirements. Most of the post-treatment isolates were, however, genetically distant from their pre-treatment counterparts, showing genotypic diversification after the treatment. Phenotypic comparisons showed a lower ODmax reached during groswth and an increased lag-time in the post-treatment isolates. All strains were capable of invasion and intracellular reproduction within A549 cultured cells. The addition of sub-inhibitory amounts (1/4 or 1/2 MIC) of tobramycin during growth showed the higher relative fitness (as a percentage of the untreated control) of the post-treatment strains. The effects of sub-MICs on biofilm growth did not show such a pronounced trend. However, when a resazurin-based viability test was applied, the advantage of the post-treatment strains was confirmed for both broth and biofilm cultures. In spite of that, according to the determined MIC values, all isolates were tobramycin-sensitive, and the data from this study imply the development of tolerance to the antibiotic in the strains that survived the treatment.

From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings

Bacterial persistence, a dormant state that enables microorganisms to survive harsh conditions, is a significant concern in food-industry settings, where traditional antimicrobial treatments often fail to eliminate these resilient cells. This article goes beyond conventional review by compiling critical information aimed at providing practical solutions to combat bacterial persisters in food production environments. This review explores the primary mechanisms behind persister cell formation, including toxin-antitoxin systems, the alarmone guanosine tetraphosphate (ppGpp), stochastic processes (in which persistence occurs as a random event), and the SOS response. Given the serious implications for food safety and quality, the authors also report a range of physical, chemical, and biological methods for targeting and eradicating persister cells. The strategies discussed, whether applied individually or in combination, offer varying levels of availability and applicability within the industry and can serve as a guide for implementing microbial contamination control plans. While significant progress has been achieved, further research is crucial to fully understand the complex mechanisms underlying bacterial persistence in food and to develop effective and targeted strategies for its eradication in food-industry settings. Overall, the translation of these insights into practical applications aims to support the food industry in overcoming this persistent challenge, ensuring safer, more sustainable food production.

What Is the Impact of Antibiotic Resistance Determinants on the Bacterial Death Rate?

Objectives: Antibiotic-resistant bacteria are widespread, with resistance arising from chromosomal mutations and resistance genes located in the chromosome or in mobile genetic elements. While resistance determinants often reduce bacterial growth rates, their influence on bacterial death under bactericidal antibiotics remains poorly understood. When bacteria are exposed to bactericidal antibiotics to which they are susceptible, they typically undergo a two-phase decline: a fast initial exponentially decaying phase, followed by a persistent slow-decaying phase. This study examined how resistance determinants affect death rates during both phases. Methods: We analyzed the death rates of ampicillin-exposed Escherichia coli populations of strains sensitive to ampicillin but resistant to nalidixic acid, rifampicin, or both, and bacteria carrying the conjugative plasmids RN3 or R702. Results: Single mutants resistant to nalidixic acid or rifampicin decayed faster than sensitive cells during the early phase, whereas the double-resistant mutant exhibited prolonged survival. These contrasting impacts suggest epistatic interactions between both chromosomal mutations. Persistent-phase death rates for chromosomal mutants did not differ significantly from wild-type cells. In contrast, plasmid-carrying bacteria displayed distinct dynamics: R702 plasmid-bearing cells showed higher persistent-phase death rates than plasmid-free cells, while RN3 plasmid-bearing cells exhibited lower rates. Conclusions: Bactericidal antibiotics may kill bacteria resistant to other antibiotics more effectively than wild-type cells. Moreover, epistasis may occur when different resistance determinants occur in the same cell, impacting the bactericidal potential of the antibiotic of choice. These results have significant implications for optimizing bacterial eradication protocols in clinical settings, as well as in animal health and industrial food safety management.

Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment

Diffusion and migration play pivotal roles in microbial communities - shaping, for example, colonization in new environments and the maintenance of spatial structures of biodiversity. While previous research has extensively studied free diffusion, such as range expansion, there remains a gap in understanding the effects of biologically or physically deleterious confined environments. In this study, we examine the interplay between migration and spatial drug heterogeneity within an experimental meta-community of E. faecalis, a Gram-positive opportunistic pathogen. When the community is confined to spatially-extended habitats ('islands') bordered by deleterious conditions, we find that the population level response depends on the trade-off between the growth rate within the island and the rate of transfer into regions with harsher conditions, a phenomenon we explore by modulating antibiotic concentration within the island. In heterogeneous islands, composed of spatially patterned patches that support varying levels of growth, the population's fate depends critically on the specific spatial arrangement of these patches - the same spatially averaged growth rate leads to diverging responses. These results are qualitatively captured by simple simulations, and analytical expressions which we derive using first-order perturbation approximations to reaction-diffusion models with explicit spatial dependence. Among all possible spatial arrangements, our theoretical and experimental findings reveal that the arrangement with the highest growth rates at the center most effectively mitigates population decline, while the arrangement with the lowest growth rates at the center is the least effective. Extending this approach to more complex experimental communities with varied spatial structures, such as a ring-structured community, further validates the impact of spatial drug arrangement. Our findings suggest new approaches to interpreting diverging clinical outcomes when applying identical drug doses and inform the possible optimization of spatially-explicit dosing strategies.

A high-throughput assay identifies molecules with antimicrobial activity against persister cells

Introduction. Persister cells are transiently non-growing antibiotic-tolerant bacteria that cause infection relapse, and there is no effective antibiotic therapy to tackle these infections.Gap statement. High-throughput assays in drug discovery are biased towards detecting drugs that inhibit bacterial growth rather than killing non-growing bacteria. A new and simple assay to discover such drugs is needed.Aim. This study aims to develop a simple and high-throughput assay to identify compounds with antimicrobial activity against persister cells and use it to identify molecular motifs with such activity.Methodology. We quantified Staphylococcus aureus persister cells by enumeration of colony forming units after 24 h ciprofloxacin treatment. We first quantified how the cell concentration, antibiotic concentration, growth phase and presence/absence of nutrients during antibiotic exposure affected the fraction of persister cells in a population. After optimizing these parameters, we screened the antimicrobial activity of compound fragments to identify molecular structures that have activity against persister cells.Results. Exponential- and stationary-phase cultures transferred to nutrient-rich media displayed a bi-phasic time-kill curve and contained 0.001-0.07% persister cells. A short rifampicin treatment resulted in 100% persister cells for 7 h, after which cells resumed activity and became susceptible. Stationary-phase cultures displayed a low but constant death rate but ultimately resulted in similarly low survival rates as the exponential-phase cultures after 24 h ciprofloxacin treatment. The persister phenotype was only maintained in most of the population for 24 h if cells were transferred to a carbon-free minimal medium before exposure to ciprofloxacin. Keeping cells starved enabled the generation of high concentrations of S. aureus cells that tolerate 50× MIC ciprofloxacin, and we used this protocol for rapid screening for biocidal antibiotics. We identified seven compounds from four structural clusters with activity against antibiotic-tolerant S. aureus. Two compounds were moderately cytotoxic, and the rest were highly cytotoxic.Conclusion. Transferring a stationary-phase culture to a carbon-free minimal medium for antimicrobial testing is a simple strategy for high-throughput screening for new antibiotics that kill persister cells. We identified molecule fragments with such activity, but further screening is needed to identify motifs with lower general cytotoxicity.

Microbial life in slow and stopped lanes

Microbes in nature often lack nutrients and face extreme or widely fluctuating temperatures, unlike microbes in growth-optimized settings in laboratories that much of the literature examines. Slowed or suspended lives are the norm for microbes. Studying them is important for understanding the consequences of climate change and for addressing fundamental questions about life: are there limits to how slowly a cell's life can progress, and how long cells can remain viable without self-replicating? Recent studies began addressing these questions with single-cell-level measurements and mathematical models. Emerging principles that govern slowed or suspended lives of cells - including lives of dormant spores and microbes at extreme temperatures - are re-defining discrete cellular states as continuums and revealing intracellular dynamics at new timescales. Nearly inactive, lifeless-appearing microbes are transforming our understanding of life.

2-Stage microfermentations

Cell based factories can be engineered to produce a wide variety of products. Advances in DNA synthesis and genome editing have greatly simplified the design and construction of these factories. It has never been easier to generate hundreds or even thousands of cell factory strain variants for evaluation. These advances have amplified the need for standardized, higher throughput means of evaluating these designs. Toward this goal, we have previously reported the development of engineered E. coli strains and associated 2-stage production processes to simplify and standardize strain engineering, evaluation and scale up. This approach relies on decoupling growth (stage 1), from production, which occurs in stationary phase (stage 2). Phosphate depletion is used as the trigger to stop growth as well as induce heterologous expression. Here, we describe in detail the development of protocols for the evaluation of engineered E. coli strains in 2-stage microfermentations. These protocols are readily adaptable to the evaluation of strains producing a wide variety of protein as well as small molecule products. Additionally, by detailing the approach to protocol development, these methods are also adaptable to additional cellular hosts, as well as other 2-stage processes with various additional triggers.

Methionyl-tRNA synthetase synthetic and proofreading activities are determinants of antibiotic persistence

Bacterial antibiotic persistence is a phenomenon where bacteria are exposed to an antibiotic and the majority of the population dies while a small subset enters a low metabolic, persistent, state and are able to survive. Once the antibiotic is removed the persistent population can resuscitate and continue growing. Several different molecular mechanisms and pathways have been implicated in this phenomenon. A common mechanism that may underly bacterial antibiotic persistence is perturbations in protein synthesis. To investigate this mechanism, we characterized four distinct metG mutants for their ability to increase antibiotic persistence. Two metG mutants encode changes near the catalytic site of MetRS and the other two mutants changes near the anticodon binding domain. Mutations in metG are of particular interest because MetRS is responsible for aminoacylation both initiator tRNAMet and elongator tRNAMet indicating that these mutants could impact translation initiation and/or translation elongation. We observed that all the metG mutants increased the level of antibiotic persistence as did reduced transcription levels of wild type metG. Although, the MetRS variants did not have an impact on MetRS activity itself, they did reduce translation rates. It was also observed that the MetRS variants affected the proofreading mechanism for homocysteine and that these mutants' growth is hypersensitive to homocysteine. Taken together with previous findings, our data indicate that both reductions in cellular Met-tRNAMet synthetic capacity and reduced proofreading of homocysteine by MetRS variants are positive determinants for bacterial antibiotic persistence.

Toxin/antitoxin systems induce persistence and work in concert with restriction/modification systems to inhibit phage

IMPORTANCE

To date, there are no reports of phage infection-inducing persistence. Therefore, our results are important since we show for the first time that a phage-defense system, the MqsRAC toxin/antitoxin system, allows the host to survive infection by forming persister cells, rather than inducing cell suicide. Moreover, we demonstrate that the MqsRAC system works in concert with restriction/modification systems. These results imply that if phage therapy is to be successful, anti-persister compounds need to be administered along with phages.

Single-cell patterning and characterisation of antibiotic persistent bacteria using bio-sCAPA

In microbiology, accessing single-cell information within large populations is pivotal. Here we introduce bio-sCAPA, a technique for patterning bacterial cells in defined geometric arrangements and monitoring their growth in various nutrient environments. We demonstrate bio-sCAPA with a study of subpopulations of antibiotic-tolerant bacteria, known as persister cells, which can survive exposure to high doses of antibiotics despite lacking any genetic resistance to the drug. Persister cells are associated with chronic and relapsing infections, yet are difficult to study due in part to a lack of scalable, single-cell characterisation methods. As >105 cells can be patterned on each template, and multiple templates can be patterned in parallel, bio-sCAPA allows for very rare population phenotypes to be monitored with single-cell precision across various environmental conditions. Using bio-sCAPA, we analysed the phenotypic characteristics of single Staphylococcus aureus cells tolerant to flucloxacillin and rifampicin killing. We find that antibiotic-tolerant S. aureus cells do not display significant heterogeneity in growth rate and are instead characterised by prolonged lag-time phenotypes alone.

Bet-hedging: Bacterial ribosome dynamics during growth transitions

It is known that bacteria reduce their ribosome numbers during nutrient starvation. New research shows that this regulation leads to the formation of two subpopulations with distinct ribosomal RNA levels. The distinct levels affect the growth recovery when nutrients become available, suggesting a possible bet-hedging strategy.

Ribosome inactivation by Escherichia coli GTPase RsgA inhibits T4 phage

Introduction

Bacteria must combat phages, and myriad bacterial anti-phage systems have been discovered that reduce host metabolism, for example, by depleting energetic compounds like ATP and NAD+. Hence, these systems indirectly inhibit protein production. Surprisingly, direct reduction of ribosome activity has not been demonstrated to thwart phage.

Methods

Here, by producing each of the 4,287 Escherichia coli proteins and selecting for anti-phage activity that leads to enhanced growth, we investigated the role of host proteins in phage inhibition.

Results and discussion

We identified that E. coli GTPase RsgA inhibits lytic phage T4 by inactivating ribosomes.

Time series and power law analysis of crop yield in some east African countries

We carry out a time series analysis on the yearly crop yield data in six east African countries (Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda) using the autoregressive integrated moving average (ARIMA) model. We describe the upper tail of the yearly crop yield data in those countries using the power law, lognormal, Fréchet and stretched exponential distributions. The forecast of the fitted ARIMA models suggests that the majority of the crops in different countries will experience neither an increase nor a decrease in yield from 2019 to 2028. A few exceptional cases correspond to significant increase in the yield of sorghum and coffee in Burundi and Rwanda, respectively, and significant decrease in the yield of beans in Burundi, Kenya and Rwanda. Based on Vuong's similarity test p-value, we find that the power law distribution captured the upper tails of yield distribution better than other distributions with just one exceptional case in Uganda, suggesting that these crops have the tendency for producing high yield. We find that only sugar cane in Somalia and sweet potato in Tanzania have the potential of producing extremely high yield. We describe the yield behaviour of these two crops as black swan, where the "rich getting richer" or the "preferential attachment" could be the underlying generating process. Other crops in Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda can only produce high but not extremely high yields. Various climate adaptation/smart strategies (use of short-duration pigeon pea varieties, use of cassava mosaic disease resistant cassava varieties, use of improved maize varieties, intensive manuring with a combination of green and poultry manure, early planting, etc) that could be adapted to increase yields in east Africa are suggested. The paper could be useful for future agricultural planning and rates calibration in crop risk insurance.

Mapping single‐cell responses to population‐level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters.

Resuscitation dynamics reveal persister partitioning after antibiotic treatment

Bacteria can survive antibiotics by forming dormant, drug-tolerant persisters. Persisters can resuscitate from dormancy after treatment and prolong infections. Resuscitation is thought to occur stochastically, but its transient, single-cell nature makes it difficult to investigate. We tracked the resuscitation of individual persisters by microscopy after ampicillin treatment and, by characterizing their dynamics, discovered that Escherichia coli and Salmonella enterica persisters resuscitate exponentially rather than stochastically. We demonstrated that the key parameters controlling resuscitation map to the ampicillin concentration during treatment and efflux during resuscitation. Consistently, we observed many persister progeny have structural defects and transcriptional responses indicative of cellular damage, for both β-lactam and quinolone antibiotics. During resuscitation, damaged persisters partition unevenly, generating both healthy daughter cells and defective ones. This persister partitioning phenomenon was observed in S. enterica, Klebsiella pneumoniae, Pseudomonas aeruginosa, and an E. coli urinary tract infection (UTI) isolate. It was also observed in the standard persister assay and after in situ treatment of a clinical UTI sample. This study reveals novel properties of resuscitation and indicates that persister partitioning may be a survival strategy in bacteria that lack genetic resistance.

The Impact of Non-Pathogenic Bacteria on the Spread of Virulence and Resistance Genes

This review discusses the fate of antimicrobial resistance and virulence genes frequently present among microbiomes. A central concept in epidemiology is the mean number of hosts colonized by one infected host in a population of susceptible hosts: R₀. It characterizes the disease's epidemic potential because the pathogen continues its propagation through susceptible hosts if it is above one. R₀ is proportional to the average duration of infections, but non-pathogenic microorganisms do not cause host death, and hosts do not need to be rid of them. Therefore, commensal bacteria may colonize hosts for prolonged periods, including those harboring drug resistance or even a few virulence genes. Thus, their R₀ is likely to be (much) greater than one, with peculiar consequences for the spread of virulence and resistance genes. For example, computer models that simulate the spread of these genes have shown that their diversities should correlate positively throughout microbiomes. Bioinformatics analysis with real data corroborates this expectation. Those simulations also anticipate that, contrary to the common wisdom, human's microbiomes with a higher diversity of both gene types are the ones that took antibiotics longer ago rather than recently. Here, we discuss the mechanisms and robustness behind these predictions and other public health consequences.

Slowest possible replicative life at frigid temperatures for yeast

Determining whether life can progress arbitrarily slowly may reveal fundamental barriers to staying out of thermal equilibrium for living systems. By monitoring budding yeast's slowed-down life at frigid temperatures and with modeling, we establish that Reactive Oxygen Species (ROS) and a global gene-expression speed quantitatively determine yeast's pace of life and impose temperature-dependent speed limits - shortest and longest possible cell-doubling times. Increasing cells' ROS concentration increases their doubling time by elongating the cell-growth (G1-phase) duration that precedes the cell-replication (S-G2-M) phase. Gene-expression speed constrains cells' ROS-reducing rate and sets the shortest possible doubling-time. To replicate, cells require below-threshold concentrations of ROS. Thus, cells with sufficiently abundant ROS remain in G1, become unsustainably large and, consequently, burst. Therefore, at a given temperature, yeast's replicative life cannot progress arbitrarily slowly and cells with the lowest ROS-levels replicate most rapidly. Fundamental barriers may constrain the thermal slowing of other organisms' lives.

Cellular memory of rapid growth is sensitive to nutrient depletion during starvation

Bacteria frequently encounter nutrient fluctuations in natural environments, yet we understand little about their ability to maintain physiological memory of previous food sources. Starvation is a particularly acute case, in which cells must balance adaptation to stresses with limited nutrient supply. Here, we show that Escherichia coli cells immediately accelerate and decelerate in growth upon transitions from spent to fresh media and vice versa, respectively, and memory of rapid growth can be maintained for many hours under constant flow of spent medium. However, after transient exposure of stationary-phase cells to fresh medium, subsequent aerobic incubation in increasingly spent medium led to lysis and limited growth when rejuvenated in fresh medium. Growth defects were avoided by incubation in anaerobic spent medium or water, suggesting that defects were caused by respiration during the process of nutrient depletion in spent medium. These findings highlight the importance of respiration for stationary phase survival and underscore the broad range of starvation outcomes depending on environmental history.

A leader cell triggers end of lag phase in populations of Pseudomonas fluorescens

The relationship between the number of cells colonizing a new environment and time for resumption of growth is a subject of long-standing interest. In microbiology this is known as the "inoculum effect." Its mechanistic basis is unclear with possible explanations ranging from the independent actions of individual cells, to collective actions of populations of cells. Here, we use a millifluidic droplet device in which the growth dynamics of hundreds of populations founded by controlled numbers of Pseudomonas fluorescens cells, ranging from a single cell, to one thousand cells, were followed in real time. Our data show that lag phase decreases with inoculum size. The decrease of average lag time and its variance across droplets, as well as lag time distribution shapes, follow predictions of extreme value theory, where the inoculum lag time is determined by the minimum value sampled from the single-cell distribution. Our experimental results show that exit from lag phase depends on strong interactions among cells, consistent with a "leader cell" triggering end of lag phase for the entire population.

Effect of NaCl, high iron, iron chelator and antibiotics on growth, virulence gene expression and drug susceptibility in non-typhoidal Salmonella: an in vitro fitness study

Salmonella is one among the most versatile and resilient enteric pathogens that is known to have developed various survival strategies within the host system. The ability of the bacteria to circumvent the physiological parameters as well as dodge the antimicrobial stress environment within the host is one of the most crucial steps in establishing an infection. With an alarming rise in multi-drug resistant serovars of non-typhoidal Salmonella and lack of vaccine for combatting the infections, behaviour of the bacteria in the presence of host physiological conditions (NaCl, high and low iron) and antibiotics will help in understanding the survival strategies as well as mechanisms of resistance. Two multi-drug resistant and two sensitive serovars of Salmonella Weltevreden and Salmonella Newport isolated from poultry and seafood were used for growth kinetics and virulence gene expression study. The results obtained revealed that despite similar resistance pattern, effect of individual class of antibiotics on the growth of serovars varied. On the contrary, no significant difference was observed in growth pattern on exposure to these in vitro experimental conditions. Nevertheless, coupling these conditions with antibiotics drastically reduced the minimum inhibitory concentration (MIC) of antibiotics in resistant strains. A first of its kind study that draws attention on the significant effect of antibiotics and physiological conditions on MIC between resistant and sensitive non-typhoidal Salmonella serovars and expression of virulence genes from Salmonella pathogenicity island (SPI) 1 and 2 (invA, hilC, fliC2, sseA and ssrB).

Gold nanocluster adjuvant enables the eradication of persister cells by antibiotics and abolishes the emergence of resistance

Persister cells are responsible for relapses of infections common in cystic fibrosis and chronic suppurative otitis media (CSOM). Yet, there are no Food and Drug Administration (FDA) approved antibiotics to eradicate persister cells. Frustratingly, the global preclinical bacterial pipeline does not contain antibacterial agents targeting persister cells. Therefore, we report a nontraditional antimicrobial chemotherapy strategy based on gold nanoclusters adjuvant to eradicate persister cells by existing antibiotics belonging to that different class. Compared to killing with antibiotics alone, combining antibiotics and AuNC@CPP sterilizes persister cells and biofilms. Enhanced killing of up to 4 orders of magnitude in a validated mouse model of CSOM with Pseudomonas aeruginosa infection was observed when combining antibiotics and AuNC@CPP, informing a potential approach to improve the treatment of CSOM. We established that the mechanism of action of AuNC@CPP is due to disruption of the proton gradient and membrane hyperpolarization. The method presented here could compensate for the lack of new antibiotics to combat persister cells. This method could also benefit the current effort to slow resistance development because AuNC@CPP abolished the emergence of drug-resistant strains induced by antibiotics.

Quantifying the impact of ecological memory on the dynamics of interacting communities

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications.

Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity.

Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

An ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and further examine our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling long-term memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

Drug-dependent growth curve reshaping reveals mechanisms of antifungal resistance in Saccharomyces cerevisiae

Microbial drug resistance is an emerging global challenge. Current drug resistance assays tend to be simplistic, ignoring complexities of resistance manifestations and mechanisms, such as multicellularity. Here, we characterize multicellular and molecular sources of drug resistance upon deleting the AMN1 gene responsible for clumping multicellularity in a budding yeast strain, causing it to become unicellular. Computational analysis of growth curve changes upon drug treatment indicates that the unicellular strain is more sensitive to four common antifungals. Quantitative models uncover entwined multicellular and molecular processes underlying these differences in sensitivity and suggest AMN1 as an antifungal target in clumping pathogenic yeasts. Similar experimental and mathematical modeling pipelines could reveal multicellular and molecular drug resistance mechanisms, leading to more effective treatments against various microbial infections and possibly even cancers.

Combined growth curve experiments and quantitative modeling reveal antifungal responses of planktonic yeast, providing a future framework to examine antimicrobial drug resistance.

Existence of log-phase Escherichia coli persisters and lasting memory of a starvation pulse

The authors characterize the growth condition dependence of survival of bacteria exposed to lethal antibiotics for a week. 1-h starvation pulse is shown to cause an increase in survival for days.

The vast majority of a bacterial population is killed when treated with a lethal concentration of antibiotics. The time scale of this killing is often comparable with the bacterial generation time before the addition of antibiotics. Yet, a small subpopulation typically survives for an extended period. However, the long-term killing dynamics of bacterial cells has not been fully quantified even in well-controlled laboratory conditions. We constructed a week-long killing assay and followed the survival fraction of Escherichia coli K12 exposed to a high concentration of ciprofloxacin. We found that long-term survivors were formed during exponential growth, with some cells surviving at least 7 d. The long-term dynamics contained at least three time scales, which greatly enhances predictions of the population survival time compared with the biphasic extrapolation from the short-term behavior. Furthermore, we observed a long memory effect of a brief starvation pulse, which was dependent on the (p)ppGpp synthase relA. Specifically, 1 h of carbon starvation before antibiotics exposure increased the surviving fraction by nearly 100-fold even after 4 d of ciprofloxacin treatment.

Observation of universal ageing dynamics in antibiotic persistence

Stress responses allow cells to adapt to changes in external conditions by activating specific pathways¹. Here we investigate the dynamics of single cells that were subjected to acute stress that is too strong for a regulated response but not lethal. We show that when the growth of bacteria is arrested by acute transient exposure to strong inhibitors, the statistics of their regrowth dynamics can be predicted by a model for the cellular network that ignores most of the details of the underlying molecular interactions. We observed that the same stress, applied either abruptly or gradually, can lead to totally different recovery dynamics. By measuring the regrowth dynamics after stress exposure on thousands of cells, we show that the model can predict the outcome of antibiotic persistence measurements. Our results may account for the ubiquitous antibiotic persistence phenotype², as well as for the difficulty in attempts to link it to specific genes³. More generally, our approach suggests that two different cellular states can be observed under stress: a regulated state, which prepares cells for fast recovery, and a disrupted cellular state due to acute stress, with slow and heterogeneous recovery dynamics. The disrupted state may be described by general properties of large random networks rather than by specific pathway activation. Better understanding of the disrupted state could shed new light on the survival and evolution of cells under stress.

Quantitative biology of survival under antibiotic treatments

The mathematical formulation for the dynamics of growth reduction and/or killing under antibiotic treatments has a long history. Even before the extensive use of antibiotics, attempts to model the killing dynamics of biocides were made [1]. Here, we review relatively simple quantitative formulations of the two main modes of survival under antibiotics, resistance and tolerance, as well as their heterogeneity in bacterial populations. We focus on the two main types of heterogeneity that have been described: heteroresistance and antibiotic persistence, each linked to the variation in a different parameter of the antibiotic response dynamics. Finally, we review the effects on survival of combining resistance and tolerance mutations as well as on the mode and tempo of evolution under antibiotic treatments.

Bacterial persistence is essential for susceptible cell survival in indirect resistance, mainly for lower cell densities

Antibiotic-susceptible bacteria may survive bactericidal antibiotics if other co-inhabiting bacteria detoxify the medium through antibiotic degradation or modification, a phenomenon denominated as indirect resistance. However, it is unclear how susceptible cells survive while the medium is still toxic. One explanation relies on the speed of detoxification, and another, non-exclusive explanation, relies on persistence, a state of bacterial dormancy where cells with low metabolic activity and growth rates are phenotypically tolerant to antibiotics and other cytotoxic substances. Here we simulated the fate of susceptible cells in laboratory experiments in the context of indirect resistance to understand whether persistence is necessary to explain the survival of susceptible cells. Depending on the strain and experimental conditions, the decay of persister populations may follow an exponential or a power-law distribution. Therefore, we studied the impact of both distributions in the simulations. Moreover, we studied the impact of considering that persister cells have a mechanism to sense the presence of a toxic substance-a mechanism that would enable cells to leave the dormant state when the medium becomes nontoxic. The simulations show that surviving susceptible cells under indirect resistance may originate both from persister and non-persister populations if the density of detoxifying cells is high. However, persistence was necessary when the initial density of detoxifying cells was low, although persister cells remained in that dormancy state for just a few hours. Finally, the results of our simulations are consistent both with exponential and power-law decay of the persistence population. Whether indirect resistance involves persistence should impact antibiotic treatments.

Phenotypic-dependent variability and the emergence of tolerance in bacterial populations

Ecological and evolutionary dynamics have been historically regarded as unfolding at broadly separated timescales. However, these two types of processes are nowadays well-documented to intersperse much more tightly than traditionally assumed, especially in communities of microorganisms. Advancing the development of mathematical and computational approaches to shed novel light onto eco-evolutionary problems is a challenge of utmost relevance. With this motivation in mind, here we scrutinize recent experimental results showing evidence of rapid evolution of tolerance by lag in bacterial populations that are periodically exposed to antibiotic stress in laboratory conditions. In particular, the distribution of single-cell lag times-i.e., the times that individual bacteria from the community remain in a dormant state to cope with stress-evolves its average value to approximately fit the antibiotic-exposure time. Moreover, the distribution develops right-skewed heavy tails, revealing the presence of individuals with anomalously large lag times. Here, we develop a parsimonious individual-based model mimicking the actual demographic processes of the experimental setup. Individuals are characterized by a single phenotypic trait: their intrinsic lag time, which is transmitted with variation to the progeny. The model-in a version in which the amplitude of phenotypic variations grows with the parent's lag time-is able to reproduce quite well the key empirical observations. Furthermore, we develop a general mathematical framework allowing us to describe with good accuracy the properties of the stochastic model by means of a macroscopic equation, which generalizes the Crow-Kimura equation in population genetics. Even if the model does not account for all the biological mechanisms (e.g., genetic changes) in a detailed way-i.e., it is a phenomenological one-it sheds light onto the eco-evolutionary dynamics of the problem and can be helpful to design strategies to hinder the emergence of tolerance in bacterial communities. From a broader perspective, this work represents a benchmark for the mathematical framework designed to tackle much more general eco-evolutionary problems, thus paving the road to further research avenues.

Active Efflux Leads to Heterogeneous Dissipation of Proton Motive Force by Protonophores in Bacteria

Various toxic compounds disrupt bacterial physiology. While bacteria harbor defense mechanisms to mitigate the toxicity, these mechanisms are often coupled to the physiological state of the cells and become ineffective when the physiology is severely disrupted. Here, we characterized such feedback by exposing Escherichia coli to protonophores. Protonophores dissipate the proton motive force (PMF), a fundamental force that drives physiological functions. We found that E. coli cells responded to protonophores heterogeneously, resulting in bimodal distributions of cell growth, substrate transport, and motility. Furthermore, we showed that this heterogeneous response required active efflux systems. The analysis of underlying interactions indicated the heterogeneous response results from efflux-mediated positive feedback between PMF and protonophores' action. Our studies have broad implications for bacterial adaptation to stress, including antibiotics. IMPORTANCE An electrochemical proton gradient across the cytoplasmic membrane, alternatively known as proton motive force, energizes vital cellular processes in bacteria, including ATP synthesis, nutrient uptake, and cell division. Therefore, a wide range of organisms produce the agents that collapse the proton motive force, protonophores, to gain a competitive advantage. Studies have shown that protonophores have significant effects on microbial competition, host-pathogen interaction, and antibiotic action and resistance. Furthermore, protonophores are extensively used in various laboratory studies to perturb bacterial physiology. Here, we have characterized cell growth, substrate transport, and motility of Escherichia coli cells exposed to protonophores. Our findings demonstrate heterogeneous effects of protonophores on cell physiology and the underlying mechanism.

Power laws in the Roman Empire: a survival analysis

The Roman Empire shaped western civilization, and many Roman principles are embodied in modern institutions. Although its political institutions proved both resilient and adaptable, allowing it to incorporate diverse populations, the Empire suffered from many conflicts. Indeed, most emperors died violently, from assassination, suicide or in battle. These conflicts produced patterns in the length of time that can be identified by statistical analysis. In this paper, we study the underlying patterns associated with the reign of the Roman emperors by using statistical tools of survival data analysis. We consider all the 175 Roman emperors and propose a new power-law model with change points to predict the time-to-violent-death of the Roman emperors. This model encompasses data in the presence of censoring and long-term survivors, providing more accurate predictions than previous models. Our results show that power-law distributions can also occur in survival data, as verified in other data types from natural and artificial systems, reinforcing the ubiquity of power-law distributions. The generality of our approach paves the way to further related investigations not only in other ancient civilizations but also in applications in engineering and medicine.

When to wake up? The optimal waking-up strategies for starvation-induced persistence

Prolonged lag time can be induced by starvation contributing to the antibiotic tolerance of bacteria. We analyze the optimal lag time to survive and grow the iterative and stochastic application of antibiotics. A simple model shows that the optimal lag time can exhibit a discontinuous transition when the severeness of the antibiotic application, such as the probability to be exposed the antibiotic, the death rate under the exposure, and the duration of the exposure, is increased. This suggests the possibility of reducing tolerant bacteria by controlled usage of antibiotics application. When the bacterial populations are able to have two phenotypes with different lag times, the fraction of the second phenotype that has different lag time shows a continuous transition. We then present a generic framework to investigate the optimal lag time distribution for total population fitness for a given distribution of the antibiotic application duration. The obtained optimal distributions have multiple peaks for a wide range of the antibiotic application duration distributions, including the case where the latter is monotonically decreasing. The analysis supports the advantage in evolving multiple, possibly discrete phenotypes in lag time for bacterial long-term fitness.

Observing Bacterial Persistence at Single-Cell Resolution

Within a bacterial population, there can be a subpopulation of cells with an antibiotic-tolerant persister phenotype characterized by long lag phase. Their long lag phase necessitates long (hours or days) periods of single-cell observation to capture high-quality quantitative information about persistence. We describe a method of single-cell imaging using glass bottom dishes and a nutrient agarose pad that allows for long-term single-cell microscopy observation in a stable environment. We apply this method to characterize the lag phase and persistence of individual Escherichia coli cells.

Forming and waking dormant cells: The ppGpp ribosome dimerization persister model

Procaryotes starve and face myriad stresses. The bulk population actively resists the stress, but a small population weathers the stress by entering a resting stage known as persistence. No mutations occur, and so persisters behave like wild-type cells upon removal of the stress and regrowth; hence, persisters are phenotypic variants. In contrast, resistant bacteria have mutations that allow cells to grow in the presence of antibiotics, and tolerant cells survive antibiotics better than actively-growing cells due to their slow growth (such as that of the stationary phase). In this review, we focus on the latest developments in studies related to the formation and resuscitation of persister cells and propose the guanosine pentaphosphate/tetraphosphate (henceforth ppGpp) ribosome dimerization persister (PRDP) model for entering and exiting the persister state.

Persistence as an Optimal Hedging Strategy

Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new, to our knowledge, theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest new opportunities for experimental investigation and design. Overall, we provide a new, to our knowledge, way of conceptualizing and modeling cellular decision making in volatile environments by explicitly unifying theory from mathematical biology and finance.

The social network: Impact of host and microbial interactions on bacterial antibiotic tolerance and persistence

Antibiotics have vastly improved our quality of life since their discovery and introduction into modern medicine. Yet, widespread use and misuse have compromised the efficacy of these compounds and put our ability to cure infectious diseases in jeopardy. To defend themselves against antibiotics, bacteria have evolved an arsenal of survival strategies. In addition to acquiring mutations and genetic determinants that confer antibiotic resistance, bacteria can respond to environmental cues and adopt reversible phenotypic changes that transiently enhance their ability to survive adverse conditions, including those brought on by antibiotics. These antibiotic tolerant and persistent bacteria, which are prevalent in biofilms and can survive antimicrobial therapy without inheriting resistance, are thought to underlie treatment failure and infection relapse. At infection sites, bacteria encounter a range of signals originating from host immunity and the local microbiota that can induce transcriptomic and metabolic reprogramming. In this review, we will focus on the impact of host factors and microbial interactions on antibiotic tolerance and persistence. We will also outline current efforts in leveraging the knowledge of host-microbe and microbe-microbe interactions in designing therapies that potentiate antibiotic activity and reduce the burden caused by recurrent infections.

A Quantitative Survey of Bacterial Persistence in the Presence of Antibiotics: Towards Antipersister Antimicrobial Discovery

Background: Bacterial persistence to antibiotics relates to the phenotypic ability to survive lethal concentrations of otherwise bactericidal antibiotics. The quantitative nature of the time-kill assay, which is the sector's standard for the study of antibiotic bacterial persistence, is an invaluable asset for global, unbiased, and cross-species analyses. Methods: We compiled the results of antibiotic persistence from antibiotic-sensitive bacteria during planktonic growth. The data were extracted from a sample of 187 publications over the last 50 years. The antibiotics used in this compilation were also compared in terms of structural similarity to fluorescent molecules known to accumulate in Escherichia coli. Results: We reviewed in detail data from 54 antibiotics and 36 bacterial species. Persistence varies widely as a function of the type of antibiotic (membrane-active antibiotics admit the fewest), the nature of the growth phase and medium (persistence is less common in exponential phase and rich media), and the Gram staining of the target organism (persistence is more common in Gram positives). Some antibiotics bear strong structural similarity to fluorophores known to be taken up by E. coli, potentially allowing competitive assays. Some antibiotics also, paradoxically, seem to allow more persisters at higher antibiotic concentrations. Conclusions: We consolidated an actionable knowledge base to support a rational development of antipersister antimicrobials. Persistence is seen as a step on the pathway to antimicrobial resistance, and we found no organisms that failed to exhibit it. Novel antibiotics need to have antipersister activity. Discovery strategies should include persister-specific approaches that could find antibiotics that preferably target the membrane structure and permeability of slow-growing cells.